{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5b7828e4-6f35-42ad-b2b2-bbcd4534ba66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模拟订单数据条数： 1000\n",
      "   id internal_order_number online_order_number store_name  \\\n",
      "0   1          IN2021000001        ON2021000001        折扣店   \n",
      "1   2          IN2021000002        ON2021000002      官方自营店   \n",
      "2   3          IN2021000003        ON2021000003      官方自营店   \n",
      "3   4          IN2021000004        ON2021000004        专营店   \n",
      "4   5          IN2021000005        ON2021000005        专营店   \n",
      "\n",
      "               full_channel_user_id       shipping_date        payment_date  \\\n",
      "0  2da37826cb2a4e22ae535edd45da1452 2021-03-28 08:37:28 2021-03-25 22:37:28   \n",
      "1  6c6652a8be1b45c686c78a92090fe62a 2021-01-12 05:29:42 2021-01-11 13:29:42   \n",
      "2  f85d46770fd04d008d849e52dba26a15 2021-02-15 04:11:10 2021-02-12 05:11:10   \n",
      "3  19fec7101556467aab87c438456a0c9f 2021-01-25 12:39:24 2021-01-22 22:39:24   \n",
      "4  7d8d4d92a0114b0b958044e001ff72f7 2021-03-24 04:41:13 2021-03-24 02:41:13   \n",
      "\n",
      "   payable_amount  paid_amount status  ... unit_price product_name  \\\n",
      "0          728.37       728.37    待付款  ...     728.37        夏季连衣裙   \n",
      "1         1333.05      1333.05    已完成  ...     444.35        夏季连衣裙   \n",
      "2         1337.84      1337.84    待发货  ...     334.46         休闲T恤   \n",
      "3         1615.26      1615.26    待发货  ...     538.42         风衣外套   \n",
      "4          452.33       452.33    已发货  ...     452.33         风衣外套   \n",
      "\n",
      "  color_and_spec product_amount original_price is_gift sub_order_status  \\\n",
      "0           白色/M         728.37        1051.08       否              已取消   \n",
      "1           白色/M        1333.05        1793.89       是              已取消   \n",
      "2           白色/L        1337.84        1597.60       是              已关闭   \n",
      "3           白色/L        1615.26        1848.71       否              已取消   \n",
      "4           白色/M         452.33         491.91       否              已关闭   \n",
      "\n",
      "  refund_status registered_quantity actual_refund_quantity  \n",
      "0         未申请退款                   1                      1  \n",
      "1           退款中                   3                      2  \n",
      "2           退款中                   4                      2  \n",
      "3          退款关闭                   3                      1  \n",
      "4         未申请退款                   1                      1  \n",
      "\n",
      "[5 rows x 31 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1100x580 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from faker import Faker\n",
    "import matplotlib\n",
    "matplotlib.rcParams['font.family'] = 'SimHei'   # 黑体\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "\n",
    "\n",
    "fake = Faker(\"zh_CN\")\n",
    "\n",
    "# =========================\n",
    "# 1. 使用 Faker 生成模拟订单数据\n",
    "#    对应 erp_order 表\n",
    "# =========================\n",
    "\n",
    "regions = [\"华东\", \"西北\", \"东北\", \"华北\", \"华南\"]\n",
    "\n",
    "\n",
    "def generate_fake_erp_orders(n_per_group=100):\n",
    "    \"\"\"\n",
    "    按 区域 × 年份 生成订单数据\n",
    "    最终总记录数 = n_per_group * 5 * 2 >= 1000\n",
    "    \"\"\"\n",
    "    rows = []\n",
    "\n",
    "    order_id = 1\n",
    "\n",
    "    # 生成订单号的函数\n",
    "    def make_order_no(prefix, year, idx):\n",
    "        return f\"{prefix}{year}{idx:06d}\"\n",
    "\n",
    "    # 目标年份：2021、2022\n",
    "    for year in [2021, 2022]:\n",
    "        # 用 datetime 对象作为 Faker 的 date_time_between 参数，避免 ParseError\n",
    "        start_dt = datetime(year, 1, 1, 0, 0, 0)\n",
    "        end_dt = datetime(year, 3, 31, 23, 59, 59)\n",
    "\n",
    "        for region in regions:\n",
    "            for i in range(n_per_group):\n",
    "                # 订单时间\n",
    "                order_time = fake.date_time_between(start_date=start_dt, end_date=end_dt)\n",
    "\n",
    "                payment_date = order_time + timedelta(hours=random.randint(0, 72))\n",
    "                shipping_date = payment_date + timedelta(hours=random.randint(0, 72))\n",
    "\n",
    "                quantity = random.randint(1, 5)\n",
    "                unit_price = round(random.uniform(50, 800), 2)\n",
    "                product_amount = round(quantity * unit_price, 2)\n",
    "\n",
    "                row = {\n",
    "                    \"id\": order_id,\n",
    "                    \"internal_order_number\": make_order_no(\"IN\", year, order_id),\n",
    "                    \"online_order_number\": make_order_no(\"ON\", year, order_id),\n",
    "                    \"store_name\": random.choice([\"旗舰店\", \"官方自营店\", \"折扣店\", \"专营店\"]),\n",
    "                    \"full_channel_user_id\": fake.uuid4().replace(\"-\", \"\")[:32],\n",
    "                    \"shipping_date\": shipping_date,\n",
    "                    \"payment_date\": payment_date,\n",
    "                    \"payable_amount\": product_amount,\n",
    "                    \"paid_amount\": product_amount,\n",
    "                    \"status\": random.choice([\"已发货\", \"已完成\", \"待发货\", \"待付款\"]),\n",
    "                    \"consignee\": fake.name(),\n",
    "                    \"spu\": f\"SPU{random.randint(1000, 9999)}\",\n",
    "                    \"order_time\": order_time,\n",
    "                    \"province\": region,\n",
    "                    \"city\": fake.city_name(),\n",
    "                    \"platform\": random.choice([\"天猫\", \"京东\", \"抖音\", \"快手\", \"拼多多\"]),\n",
    "                    \"sub_order_number\": make_order_no(\"SUB\", year, order_id),\n",
    "                    \"online_sub_order_number\": make_order_no(\"SUBON\", year, order_id),\n",
    "                    \"original_online_order_number\":\n",
    "                        make_order_no(\"ORION\", year, order_id),\n",
    "                    \"sku\": f\"SKU{random.randint(100000, 999999)}\",\n",
    "                    \"quantity\": quantity,\n",
    "                    \"unit_price\": unit_price,\n",
    "                    \"product_name\": random.choice([\"夏季连衣裙\", \"牛仔裤\", \"休闲T恤\", \"风衣外套\", \"针织衫\"]),\n",
    "                    \"color_and_spec\": random.choice([\"黑色/M\", \"黑色/L\", \"白色/M\", \"白色/L\", \"蓝色/S\"]),\n",
    "                    \"product_amount\": product_amount,\n",
    "                    \"original_price\": round(product_amount * random.uniform(1.0, 1.5), 2),\n",
    "                    \"is_gift\": random.choice([\"否\", \"是\"]),\n",
    "                    \"sub_order_status\": random.choice([\"正常\", \"已取消\", \"已关闭\"]),\n",
    "                    \"refund_status\": random.choice([\"未申请退款\", \"退款中\", \"成功退款\", \"退款关闭\"]),\n",
    "                    \"registered_quantity\": quantity,\n",
    "                    \"actual_refund_quantity\": random.randint(0, quantity),\n",
    "                }\n",
    "\n",
    "                rows.append(row)\n",
    "                order_id += 1\n",
    "\n",
    "    df_erp = pd.DataFrame(rows)\n",
    "    return df_erp\n",
    "\n",
    "\n",
    "# 生成订单数据\n",
    "erp_orders = generate_fake_erp_orders(n_per_group=100)\n",
    "print(\"模拟订单数据条数：\", len(erp_orders))\n",
    "print(erp_orders.head())\n",
    "\n",
    "\n",
    "# ========================================\n",
    "# 2. 构造区域完成率数据（完全一致）\n",
    "#    并用 Matplotlib 还原 Excel 风格图\n",
    "# ========================================\n",
    "\n",
    "data = {\n",
    "    \"区域\": [\"华东\", \"西北\", \"东北\", \"华北\", \"华南\"],\n",
    "    \"2022年\": [36, 31, 18, 13, 9],\n",
    "    \"2021年\": [42, 26, 19, 12, 5],\n",
    "}\n",
    "df_region = pd.DataFrame(data)\n",
    "\n",
    "def plot_region_completion(df_region):\n",
    "    # 保持和表格一样的顺序：华东、西北、东北、华北、华南\n",
    "    # 如果 df_region 已经是这个顺序，这行其实可有可无\n",
    "    df = df_region.set_index(\"区域\").loc[[\"华东\", \"西北\", \"东北\", \"华北\", \"华南\"]].reset_index()\n",
    "\n",
    "    regions = df[\"区域\"].tolist()\n",
    "    pct_2022 = df[\"2022年\"].tolist()\n",
    "    pct_2021 = df[\"2021年\"].tolist()\n",
    "\n",
    "    n_region = len(regions)\n",
    "\n",
    "    # y 轴：0 底部，n-1 顶部；我们希望“华东”在最上面\n",
    "    y_pos = np.arange(n_region)[::-1]  # 反转一下，第一行在最上面\n",
    "\n",
    "    ticks_per_percent = 2  # 每 1% 画两根竖线\n",
    "    max_pct = max(max(pct_2022), max(pct_2021))\n",
    "    max_ticks = max_pct * ticks_per_percent\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(11, 5.8))\n",
    "\n",
    "    # 深蓝背景\n",
    "    fig.patch.set_facecolor(\"#071a3d\")\n",
    "    ax.set_facecolor(\"#071a3d\")\n",
    "\n",
    "    # ---------- 左侧：2022 ----------\n",
    "    for i, p in enumerate(pct_2022):\n",
    "        n_ticks = int(p * ticks_per_percent)\n",
    "        for t in range(n_ticks):\n",
    "            x = -(t + 1) * 0.05\n",
    "            ax.vlines(\n",
    "                x,\n",
    "                y_pos[i] - 0.18,\n",
    "                y_pos[i] + 0.18,\n",
    "                linewidth=1,\n",
    "                color=\"#1fb6ff\",\n",
    "            )\n",
    "\n",
    "    # ---------- 右侧：2021 ----------\n",
    "    for i, p in enumerate(pct_2021):\n",
    "        n_ticks = int(p * ticks_per_percent)\n",
    "        for t in range(n_ticks):\n",
    "            x = (t + 1) * 0.05\n",
    "            ax.vlines(\n",
    "                x,\n",
    "                y_pos[i] - 0.18,\n",
    "                y_pos[i] + 0.18,\n",
    "                linewidth=1,\n",
    "                color=\"#ff6f61\",\n",
    "            )\n",
    "\n",
    "    # 中间区域名\n",
    "    for i, r in enumerate(regions):\n",
    "        ax.text(\n",
    "            0,\n",
    "            y_pos[i],\n",
    "            r,\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            fontsize=13,\n",
    "            color=\"white\",\n",
    "        )\n",
    "\n",
    "    # 左右百分比\n",
    "    offset = max_ticks * 0.05 + 0.4  # 竖线最外侧再留一点空白\n",
    "    for i, p in enumerate(pct_2022):\n",
    "        ax.text(\n",
    "            -offset,\n",
    "            y_pos[i],\n",
    "            f\"{p}%\",\n",
    "            ha=\"right\",\n",
    "            va=\"center\",\n",
    "            fontsize=12,\n",
    "            color=\"white\",\n",
    "        )\n",
    "    for i, p in enumerate(pct_2021):\n",
    "        ax.text(\n",
    "            offset,\n",
    "            y_pos[i],\n",
    "            f\"{p}%\",\n",
    "            ha=\"left\",\n",
    "            va=\"center\",\n",
    "            fontsize=12,\n",
    "            color=\"white\",\n",
    "        )\n",
    "\n",
    "    # 主标题 & 副标题（往上挪一点，避免挤在一起）\n",
    "    fig.suptitle(\n",
    "        \"2022年第一季度销售目标完成情况\",\n",
    "        x=0.03,\n",
    "        y=0.92,\n",
    "        ha=\"left\",\n",
    "        fontsize=20,\n",
    "        fontweight=\"bold\",\n",
    "        color=\"white\",\n",
    "    )\n",
    "    fig.text(\n",
    "        0.03,\n",
    "        0.82,\n",
    "        \"华东区域完成率最高达到36%，但是相比去年的42%有所下降\",\n",
    "        ha=\"left\",\n",
    "        color=\"white\",\n",
    "        fontsize=12,\n",
    "    )\n",
    "\n",
    "    # 列标题（2022年 / 2021年），放在两侧竖线的正上方\n",
    "    # 用数据坐标画，更贴近竖线位置\n",
    "    top_y = y_pos.max() + 0.8\n",
    "    ax.text(\n",
    "        -offset / 2,\n",
    "        top_y,\n",
    "        \"2022年\",\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "        fontsize=12,\n",
    "        color=\"white\",\n",
    "    )\n",
    "    ax.text(\n",
    "        offset / 2,\n",
    "        top_y,\n",
    "        \"2021年\",\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "        fontsize=12,\n",
    "        color=\"white\",\n",
    "    )\n",
    "\n",
    "    # 底部数据来源\n",
    "    fig.text(\n",
    "        0.02,\n",
    "        0.04,\n",
    "        \"注：数据来源于公司销售系统，统计日期截至2022.03.31\",\n",
    "        ha=\"left\",\n",
    "        color=\"white\",\n",
    "        fontsize=10,\n",
    "    )\n",
    "\n",
    "    # 去掉轴刻度和边框\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_visible(False)\n",
    "\n",
    "    # 调整显示范围，让图形更居中、上下留白更均匀\n",
    "    ax.set_ylim(-0.5, n_region - 0.5 + 0.6)  # 上面多留一点空给列标题\n",
    "    ax.set_xlim(-offset - 0.5, offset + 0.5)\n",
    "\n",
    "    # 手动调整画布四边的空白\n",
    "    plt.subplots_adjust(left=0.08, right=0.92, top=0.82, bottom=0.12)\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# 绘制图表\n",
    "plot_region_completion(df_region)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "580c2ce2-95d6-4de6-b2c5-425a64d5709b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
